Categorize, automate, and visualize the candidate library
With the schema in place, the next prompts turn the library into a real recruiting workspace — department fits, status tracking, summary tables, even color-coded views. All conversational.
Takeaway 1
Categorization is a closed-list classification — you supply the buckets.
Takeaway 2
Automation is one sentence: "Email me when a new engineering candidate is added."
Takeaway 3
Conditional styling makes the table readable at a glance — no SharePoint Designer required.
Categorize candidates into department fits
The next ask sounds simple: "Categorize if each candidate is best suited for an HR, marketing, finance, sales, or engineering role." The AI reads every resume against that fixed list of buckets and writes a value back into a new department fit column.
Notice the structure of the prompt — the user defines the categories. The AI isn't inventing taxonomies; it's classifying into the user's taxonomy. That's what keeps the result usable downstream for filtering, grouping, and alerting.
Closed-list classification is the safe move. Hand the AI your buckets and ask it to assign — much sturdier than "figure out the right categories."
It looks like the AI is making hiring decisions. It isn't — it's a first-pass router into the buckets you defined. Final fit is still your call.
Group the library by department fit
"Group the candidates by their department fit." That's it — the SharePoint library reorganizes into collapsible department sections. Engineering candidates cluster together, sales candidates cluster together, and the view becomes scannable instead of a flat list.
Groupings are just another lens on the same data. Switch back to the flat view, sort by education, group by job title — nothing about the underlying library changes.
Views are cheap. Once metadata exists in columns, you can group, sort, or filter by it in a sentence.
"Email me when a new engineering candidate is added"
The user types one line: "Send me an email anytime a new engineering candidate is added." That installs an automation — a watcher that fires when a row in the library matches the condition.
The trigger isn't generic. It's scoped to a specific column value. Other departments stay quiet; engineering pings the inbox. Conversational automation is what makes this feel light: no flow designer, no rule editor, no IFTTT.
When the schema exists, automations are one-liners. "Email me when column X equals Y" is the entire setup.
This isn't a one-off email send — it's a persistent rule. The library keeps watching and keeps sending until you turn the rule off.
Track pipeline status with a choice field
"Add a choice field that can track whether we have sent the candidate for an interview, have offered, or they've accepted or declined." A new column lands with those exact choices, ready to be set per row.
Choice fields are how SharePoint enforces a small, controlled vocabulary. Free-text status would drift — "interview", "Interview", "1st round" — and break grouping. Choices keep the categories aligned and the views consistent.
Use choice fields whenever the values are a small, known set. They keep grouping, filtering, and reporting clean.
Summary table plus colored styling
Two final asks close the loop. First: "Create a table summarizing how many candidates of each type we've found so far." That's a count-by-department roll-up — a small dashboard built from the library's own metadata.
Second: "Style the library columns to be clearly differentiated across target roles and candidate status." Conditional column styling colors each bucket so the table reads at a glance. Engineering rows look different from sales rows. Offered candidates look different from declined.
A roll-up table and color-coded columns turn the library into a recruiter dashboard — no separate reporting tool, no separate visual layer.
"Make it pretty" doesn't help. Asking the AI to differentiate roles and statuses gives it a goal — visual distinction in service of scanability — instead of asking for decoration.
Flashcards — Applied
02 · Applied
Closed-List Categorization
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AI classification into a user-supplied set of buckets (HR, marketing, finance, sales, engineering). Sturdier than open-ended labeling because the categories are fixed.
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02 · Applied
Grouped View
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A SharePoint view that clusters rows by a column value — here, by department fit. Cheap to switch lenses; the underlying library is unchanged.
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02 · Applied
Email Trigger
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A persistent automation set by a single sentence — "Email me when a new engineering candidate is added." Watches the library and notifies on matching rows.
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02 · Applied
Choice Field
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A column with a fixed set of allowed values — interview / offered / accepted / declined. Prevents text drift and keeps grouping and reporting consistent.
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02 · Applied
Summary Roll-Up
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A count-by-category table built from library metadata. Turns the document library into a lightweight recruiting dashboard without a separate reporting tool.
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02 · Applied
Column Styling
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Conditional formatting that colors column values by role or status, making the library scannable at a glance. Asked for in plain language: "differentiate across target roles."